arXiv:2603.10468v2 Announce Type: replace-cross Abstract: We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting, chunk-wise inference must preserve meeting-level speaker identity consistency while producing time-stamped, speaker-labeled transcripts. Prior Speech-LLM systems tend to prioritize either local diarization or global labeling, lacking the ability to jointly model fine-grained temporal boundaries and robust cross-chunk identity linking. We propose G-STAR, an end-to-end framework that couples a cach
The continuous improvement in AI models for speech processing, particularly for complex multi-speaker scenarios, reflects ongoing research into more robust and efficient interaction with AI systems.
This development allows for more accurate and reliable transcription and analysis of multi-party conversations, critical for applications ranging from business meetings to legal proceedings and advanced AI agent interactions.
The ability of G-STAR to jointly model fine-grained temporal boundaries and robust cross-chunk identity linking represents a notable improvement over prior systems that prioritized either local diarization or global labeling.
- · AI software developers
- · Customer service platforms
- · Meeting transcription services
- · AI agents
- · Manual transcription services
- · Legacy speech recognition systems
Improved performance in multi-speaker automatic speech recognition (ASR) will lead to more reliable meeting summaries and corporate knowledge capture.
Enhanced SA-ASR capabilities will accelerate the development of sophisticated AI agents that can accurately understand and participate in complex human conversations.
The increased accuracy in attributing speech to specific individuals could raise new privacy concerns and debates around consent for AI-driven monitoring of conversations.
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Read at arXiv cs.AI